Conventionally, for estimation and forecasting of a yield of agricultural produce (vegetation growth analysis), an algorithm has been written based on optical remote sensing data obtained from an artificial satellite, an aircraft, etc., and vegetation growth analysis using such algorithm has been put in practical use. However, in Japan, because it has four seasons and a rainy season, and has many rainy days and cloudy days throughout the year, stable observation of the surface of the ground is difficult with optical remote sensing which is influenced by weather.
Also, when a wide variety of agricultural produce is produced in small quantities in the land of complex topography elongated from north to south as in Japan, precise agricultural management is necessary. However, there has been a circumstance so far that it is difficult to obtain high precision data based on which growth conditions can be grasped in cultivated field units, other than optical remote sensing data.
On the other hand, a synthetic aperture radar (SAR) is available as an example of the active-type sensor that measures reflective waves of electric waves (microwave pulses) irradiated to the surface of the ground. The synthetic aperture radar is capable of taking photos of a wide area of the surface of the ground, day and night, regardless of weather, using the characteristics of microwaves. Also, although the synthetic aperture radar in the past has problems in resolution (fineness) compared with optical remote sensing, some leading edge satellite-mounted-type synthetic aperture radar has the resolution of 1 m or below, and it is becoming possible to obtain high-resolution images.
Past studies on agricultural produce carried out using a synthetic aperture radar include the following:
Estimation of paddy-field rice planted acreage concurrently using optical remote sensing data and SAR images taken at multiple times of a year;
Estimation of paddy-field rice planted acreage based on multiple-wavelength and multiple-polarization SAR images;
Study on type classification of agricultural produce based on SAR images taken at multiple times of a year.
These studies were made abroad, and also many papers were published, however, they did not directly lead to growth forecasting of agricultural produce such as paddy-field rice, etc.
Then, studies are being made on monitoring growth conditions of paddy-field rice using SAR images taken at multiple times of a year (see, for example, Non-patent Document 1).
Some of the inventors of the present application have also announced a result of a study on grasping growth conditions of paddy-field rice using SAR images taken at multiple times of a year (see Non-patent Document 2). An analysis result as shown in FIG. 1 through FIG. 3 was obtained by the study described in Non-patent Document 2.
FIG. 1 shows examples of radar images of a target area taken at plural times, and specifically shows, for each observation day, growth and reflection characteristics, conditions of paddy fields, paddy-field rice covering conditions of paddy fields, and radar images. In this example, the radar images were taken by the C band of PADARSAT. Radar images 1A, 1B, 1C, and 1D at four times, i.e., May 22, June 15, July 9, and August 2, were produced, and from these radar images, it can be grasped that the growth conditions of paddy-field rice have changed on the whole.
FIG. 2 shows a relationship between paddy-field rice coverage and a radar backscatter coefficient (backscattered components of microwaves from a radar device), and the horizontal axis indicates paddy-field rice coverage (%) and the vertical axis indicates a radar backscatter coefficient (dB). As shown in FIG. 2, paddy-field rice coverage and a radar backscatter coefficient have a strong correlation, and by applying the method of least squares to data (paddy-field rice coverage and backscatter coefficients) of multiple observation points, a regression line 2 representing a relationship between paddy-field rice coverage and a radar backscatter coefficient can be obtained.
FIG. 3 shows radar backscatter coefficient distributions by cultivated field blocks at respective times. Thus, by producing radar backscatter coefficient distribution maps by cultivated field blocks at respective times 3A, 3B, 3C, and 3D using radar images, it is possible to grasp growth conditions of paddy-field rice by cultivated field blocks.
Non-patent Document 1; Zengyuan Li, Guoqing Sun, Mike Wooding, Yong Pang, Yanfang Dong, Erxue Chen and Bingxiang Tan “Rice Monitoring Using Envisat Asar Data in China”, Proc. of the 2004 Envisat & ERS Symposium, Salzburg, Austria 6-10 Sep. 2004 (ESA SP-572, April 2005)
Non-patent Document 2; Kazuyoshi Takahashi, Hiroaki Abe, Atsushi Rikimaru, Yukio Mukai, “Grasping Paddy-field Rice Growth Distributions Using Time-series RADARSAT Data”, Japan Society of Photography and Remote Sensing, Annual Academic Lectures, Tokyo Big Sight (Tokyo), Jun. 17 and 18, 2004